Gary Marcus' keynote at AGI-24

Machine Learning Street Talk (MLST)

Prof Gary Marcus revisited his keynote from AGI-21, noting that many of the issues he highlighted then are still relevant today despite significant advances in AI.

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Gary Marcus criticized current large language models (LLMs) and generative AI for their unreliability, tendency to hallucinate, and inability to truly understand concepts.

Marcus argued that the AI field is experiencing diminishing returns with current approaches, particularly the "scaling hypothesis" that simply adding more data and compute will lead to AGI.

He advocated for a hybrid approach to AI that combines deep learning with symbolic AI, emphasizing the need for systems with deeper conceptual understanding.

Marcus highlighted the importance of developing AI with innate understanding of concepts like space, time, and causality.

He expressed concern about the moral decline in Silicon Valley and the rush to deploy potentially harmful AI technologies without adequate safeguards.

Marcus predicted a possible upcoming "AI winter" due to inflated valuations, lack of profitability, and overhyped promises in the industry.

He stressed the need for better regulation of AI, including transparency in training data, full disclosure of testing, and independent auditing of AI systems.

Marcus proposed the creation of national and global AI agencies to oversee the development and deployment of AI technologies.

He concluded by emphasizing the importance of interdisciplinary collaboration, focusing on robust AI with deep understanding, and implementing smart, agile governance for AI and AGI.

YT Version (very high quality filmed)

https://youtu.be/91SK90SahHc

Pre-order Gary's new book here:

Taming Silicon Valley: How We Can Ensure That AI Works for Us

https://amzn.to/4fO46pY

Filmed at the AGI-24 conference:

https://agi-conf.org/2024/

TOC:

00:00:00 Introduction

00:02:34 Introduction by Ben G

00:05:17 Gary Marcus begins talk

00:07:38 Critiquing current state of AI

00:12:21 Lack of progress on key AI challenges

00:16:05 Continued reliability issues with AI

00:19:54 Economic challenges for AI industry

00:25:11 Need for hybrid AI approaches

00:29:58 Moral decline in Silicon Valley

00:34:59 Risks of current generative AI

00:40:43 Need for AI regulation and governance

00:49:21 Concluding thoughts

00:54:38 Q&A: Cycles of AI hype and winters

01:00:10 Predicting a potential AI winter

01:02:46 Discussion on interdisciplinary approach

01:05:46 Question on regulating AI

01:07:27 Ben G's perspective on AI winter